Deploying Scalable Agents
Moves an agent prototype to a scalable, observable deployment on Microsoft Foundry.
Install
npx skills add microsoft/ai-agents-for-beginners --skill deploying-scalable-agents -g -a claude-code -y- Repository
- microsoft/ai-agents-for-beginners78.6k
- Category
- AI and agents
- Updated
- 5d ago
- License
- MIT
- Context cost
- Lightabout 1,237 tokens
- Runs scripts
- No, instructions only
What it does
Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.
Deploying Scalable Agents with Microsoft Foundry
Companion skill for Lesson 16, Deploying Scalable Agents. Use it to help a learner move an agent from prototype to a scalable, observable production deployment. Ground every recommendation in the lesson content and the runnable notebook; do not invent Foundry APIs.
Triggers
Activate this skill when a learner wants to:
- Deploy an agent to Microsoft Foundry as a hosted agent and make it versioned/observable.
- Choose between client-hosted, hosted-agent, and agent-workflow deployment patterns.
- Add model routing, response caching, or bounded concurrency to control latency and cost.
- Add an evaluation gate so a bad agent version cannot ship.
- Add a human-in-the-loop approval step for high-risk actions.
- Instrument an agent with OpenTelemetry tracing for production observability.
- Smoke-test a deployed agent as a fast post-deploy gate.
Core mental model
A production agent is mostly the operational skeleton around the model (~80%), not the model itself. Map every recommendation to one of these concerns:
| Concern | Prototype → Production |
|---|---|
| Hosting | notebook → versioned hosted service |
| Identity | your az login → managed identity + scoped RBAC |
| State | in-memory → externalised thread/memory store |
| Failure | traceback → retries, fallbacks, alerts |
| Cost | "a few cents" → tracked, routed, cached, budgeted |
| Quality | eyeballing → automated evaluation gate |
| Trust | you approve → policy + human-in-the-loop |
